import numpy as np
import pandas as pd

def add_technical_indicators(df: pd.DataFrame) -> pd.DataFrame:
    df = df.copy()

    # RSI
    delta = df["Close"].diff()
    gain = delta.where(delta > 0, 0).rolling(window=14).mean()
    loss = -delta.where(delta < 0, 0).rolling(window=14).mean()
    rs = gain / loss
    df["RSI"] = 100 - (100 / (1 + rs))

    # MACD
    exp1 = df["Close"].ewm(span=12, adjust=False).mean()
    exp2 = df["Close"].ewm(span=26, adjust=False).mean()
    df["MACD"] = exp1 - exp2

    # Simple 50-day MA
    df["MA50"] = df["Close"].rolling(window=50).mean()

    # Log Volume
    df["Log_Volume"] = np.log(df["Volume"] + 1)

    df = df.dropna()
    return df

def classify_signal(predicted_change: float) -> str:
    if predicted_change > 1.0:
        return "STRONG BUY"
    if predicted_change > 0:
        return "BUY"
    if predicted_change < -1.0:
        return "STRONG SELL"
    if predicted_change < 0:
        return "SELL"
    return "HOLD"

def classify_volatility(df: pd.DataFrame) -> str:
    returns = df["Close"].pct_change().dropna()
    vol = returns.std()
    if vol < 0.01:
        return "Low"
    if vol < 0.025:
        return "Medium"
    return "High"